Context

This public dataset was created by the Centers for Medicare & Medicaid Services. The data summarize counts of enrollees who are dually-eligible for both Medicare and Medicaid program, including those in Medicare Savings Programs. “Duals” represent 20 percent of all Medicare beneficiaries, yet they account for 34 percent of all spending by the program, according to the Commonwealth Fund . As a representation of this high-needs, high-cost population, these data offer a view of regions ripe for more intensive care coordination that can address complex social and clinical needs. In addition to the high cost savings opportunity to deliver upstream clinical interventions, this population represents the county-by-county volume of patients who are eligible for both state level (Medicaid) and federal level (Medicare) reimbursements and potential funding streams to address unmet social needs across various programs, waivers, and other projects. The dataset includes eligibility type and enrollment by quarter, at both the state and county level. These data represent monthly snapshots submitted by states to the CMS, which are inherently lower than ever-enrolled counts (which include persons enrolled at any time during a calendar year.) For more information on dually eligible beneficiaries

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.sdoh_cms_dual_eligible_enrollment.

Sample Query

In what counties in Michigan has the number of dual-eligible individuals increased the most from 2015 to 2018? Find the counties in Michigan which have experienced the largest increase of dual enrollment households

duals_Jan_2015 AS ( SELECT Public_Total AS duals_2015, County_Name, FIPS FROM bigquery-public-data.sdoh_cms_dual_eligible_enrollment.dual_eligible_enrollment_by_county_and_program WHERE State_Abbr = "MI" AND Date = '2015-12-01' ),

duals_increase AS ( SELECT d18.FIPS, d18.County_Name, d15.duals_2015, d18.duals_2018, (d18.duals_2018 - d15.duals_2015) AS total_duals_diff FROM duals_Jan_2018 d18 JOIN duals_Jan_2015 d15 ON d18.FIPS = d15.FIPS )

SELECT * FROM duals_increase WHERE total_duals_diff IS NOT NULL ORDER BY total_duals_diff DESC

Federal law requires that state Medicaid programs make Disproportionate Share Hospital (DSH) payments to qualifying hospitals that serve a large number of Medicaid and uninsured individuals. State-specific annual DSH reports are posted as submitted by states based on their availability. For more information, visit https://www.medicaid.gov/medicaid/finance/dsh/index.html.

Analysis of ‘Disproportionate Share Hospital (DSH) Payments - Annual Reporting Requirements’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/ded26022-a55d-4b07-8c9f-9c23edc7e81d on 26 January 2022.

--- Dataset description provided by original source is as follows ---

Federal law requires that state Medicaid programs make Disproportionate Share Hospital (DSH) payments to qualifying hospitals that serve a large number of Medicaid and uninsured individuals. State-specific annual DSH reports are posted as submitted by states based on their availability.

For more information, visit https://www.medicaid.gov/medicaid/finance/dsh/index.html.

--- Original source retains full ownership of the source dataset ---

Objectives: To investigate the association of state-level Medicaid expansion and non-elderly mortality rates from 1999 to 2018 in Northeastern urban settings.Methods: This quasi-experimental study utilized a synthetic control method to assess the association of Medicaid expansion on non-elderly urban mortality rates [1999–2018]. Counties encompassing the largest cities in the Northeastern Megalopolis (Washington D.C., Baltimore, Philadelphia, New York City, and Boston) were selected as treatment units (n = 5 cities, 3,543,302 individuals in 2018). Cities in states without Medicaid expansion were utilized as control units (n = 17 cities, 12,713,768 individuals in 2018).Results: Across all cities, there was a significant reduction in the neoplasm (Population-Adjusted Average Treatment Effect = −1.37 [95% CI −2.73, −0.42]) and all-cause (Population-Adjusted Average Treatment Effect = −2.57 [95%CI −8.46, −0.58]) mortality rate. Washington D.C. encountered the largest reductions in mortality (Average Treatment Effect on All-Cause Medical Mortality = −5.40 monthly deaths per 100,000 individuals [95% CI −12.50, −3.34], −18.84% [95% CI −43.64%, −11.67%] reduction, p = < 0.001; Average Treatment Effect on Neoplasm Mortality = −1.95 monthly deaths per 100,000 individuals [95% CI −3.04, −0.98], −21.88% [95% CI −34.10%, −10.99%] reduction, p = 0.002). Reductions in all-cause medical mortality and neoplasm mortality rates were similarly observed in other cities.Conclusion: Significant reductions in urban mortality rates were associated with Medicaid expansion. Our study suggests that Medicaid expansion saved lives in the observed urban settings.

The Nationwide Inpatient Sample (NIS) is a database focused on hospital stay information. Users are able to use the NIS to identify, track, and analyze national trends in health care utilization, access, charges, quality, and outcomes. Background The Nationwide Inpatient Sample (NIS) is maintained by the Healthcare Cost and Utilization Project. The NIS is the largest all-payer inpatient care database in the United States. It contains data from approximately 8 million hospital stays each year. The 2009 NIS contains all discharge data from 1,050 hospitals located in 44 States, approximating a 20-percent stratified sample of U.S. community hospitals. The sampling frame for the 2009 NIS is a sample of hospitals that comprises approximately 95 percent of all hospital discharges in the United States. The NIS is the only national hospital database containing charge information on all patients, regardless of payer, including persons covered by Medicare, Medicaid, private insurance, and the uninsured. User functionality Users must pay to access the database. NIS releases for data years 1988-2009 are available from the HCUP Central Distributor. The 2009 NIS may be purchased for $50 for students and $350 for all others on a single DVD-ROM with accompanying documentation. . Data Notes NIS data are available from 1988 to 2009. The number of states in the NIS has grown from 8 in the first year to 44 at present. Beginning with the 2002 NIS, severity adjustment data elements including APR-DRGs, APS-DRGs, Disease Staging, and AHRQ Comorbidity Indicators, are available. Begi nning with the 2005 NIS, Diagnosis and Procedure Groups Files containing data elements from AHRQ software tools designed to facilitate the use of the ICD-9-CM diagnostic and procedure information are available. Beginning with the 2007 NIS, data elements describing hospital structural characteristics and provision of outpatient services are available in the Hospital Weights file. NIS Release 1 includes data from 8-11 States and spans the years 1988 to 1992. NIS Releases 2 and 3 contain data from 17 States for 1993 and 1994, respectively. NIS Releases 4 and 5 contain data from 19 States for 1995 and 1996. NIS Release 6 contains data from 22 States for 1997. NIS 1998 contains data from 22 States. NIS 1999 contains data from 24 States. NIS 2000 contains data from 28 States. NIS 2001 contains data from 33 States. NIS 2002 contains data from 35 States. NIS 2003 contains data from 37 States. NIS 2004 contains data from 37 States. NIS 2005 contains data from 37 States. NIS 2006 contains data from 38 States. NIS 2007 contains data from 40 States. NIS 2008 contains data from 42 States.